Abstract
Abstract. Geometric camera calibration is a mandatory prerequisite for many applications in computer vision and photogrammetry. Especially when requiring an accurate camera model the effort for calibration can increase dramatically. For the calibration of the stereo-camera used for optical navigation a new chessboard based approach is presented. It is derived from different parts of existing approaches which, taken separately, are not able to meet the requirements. Moreover, the approach adds one novel main feature: It is able to detect all visible chessboard fields with the help of one or more fiducial markers simply sticked on a chessboard (AprilTags). This allows a robust detection of one or more chessboards in a scene, even from extreme perspectives. Except for the acquisition of the calibration images the presented approach enables a fully automatic calibration. Together with the parameters of the interior and relative orientation the full covariance matrix of all model parameters is calculated and provided, allowing a consistent error propagation in the whole processing chain of the imaging system. Even though the main use case for the approach is a stereo camera system it can be used for a multi-camera system with any number of cameras mounted on a rigid frame.
Highlights
1.1 MotivationWhen deriving spatial information from images, the camera model is the key connection between the object and image coordinate space
This is important for the evaluation of the calibration results and essential to include the uncertainties of the camera model parameters in the error propagation of Integrated Positioning System (IPS)
In particular the OpenCV calibration tools (Bradski, 2010), the camera calibration toolbox implemented in MATLAB R by Bouguet (2015) and developed by the German aerospace center (DLR) Camera Calibration Toolbox (Strobl et al, 2010; Strobl and Hirzinger, 2008, 2011a), which is implemented in IDL R, are used for the following comparison
Summary
When deriving spatial information from images, the camera model is the key connection between the object and image coordinate space. The required accuracy strongly depends on the particular application This is why it should be evaluated carefully which camera calibration approach is used in order to reach the desired accuracy. It is clearly preferable to use an own implementation of the bundle adjustment that provides the full covariance matrix of the determined camera model parameters This is important for the evaluation of the calibration results and essential to include the uncertainties of the camera model parameters in the error propagation of IPS. The lack of such a solution was one main motivation for an individual implementation
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